Updated: 2020-08-12 07:44:15 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
South Dakota 1.17 9554 102
North Dakota 1.14 7837 147
West Virginia 1.12 7946 142
Arkansas 1.10 49305 835
Idaho 1.10 25965 516
Illinois 1.10 198040 1841
Indiana 1.10 77774 985
Virginia 1.09 81365 930
Kansas 1.08 32253 435
Georgia 1.07 204683 3565
Kentucky 1.07 37743 677
Montana 1.06 5130 120
Texas 1.06 527910 8482
Minnesota 1.05 62143 754
Oregon 1.05 21946 335
Wisconsin 1.05 62569 906
California 1.04 583941 8063
Iowa 1.04 49780 484
Nebraska 1.04 28944 288
Tennessee 1.04 124089 2122
Alabama 1.03 104997 1650
Missouri 1.03 55370 1090
Vermont 1.03 1456 5
Washington 1.03 67041 785
Michigan 1.02 98181 731
Ohio 1.02 103557 1222
Oklahoma 1.02 45660 907
Massachusetts 1.01 121477 390
Nevada 1.01 58552 975
Maryland 1.00 97895 847
North Carolina 1.00 139618 1640
Pennsylvania 1.00 125303 821
New York 0.99 426855 649
Utah 0.99 45109 440
Colorado 0.98 51826 467
Louisiana 0.98 134636 1670
Mississippi 0.98 69702 1077
New Jersey 0.98 186740 368
South Carolina 0.98 103399 1328
Delaware 0.97 15517 85
Florida 0.97 549529 7497
New Hampshire 0.97 6882 28
Wyoming 0.95 3110 38
New Mexico 0.94 22836 214
Rhode Island 0.92 18027 74
Maine 0.89 4074 15
Connecticut 0.88 50476 96
Arizona 0.87 191272 1522

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

\(R_e\) Trend

National effective reproduction rate

Distribution of \(R_e\) Values

Howver, there is a wiude dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

Distribution of Baseline Control

Similarly for disease control, when take at the county level, there is a wide distribution of Baseline Control.

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
King WA 1 1 1.0 16952 780 162
Pierce WA 3 2 1.1 6470 750 109
Spokane WA 5 3 1.1 4546 910 84
Grant WA 9 4 1.2 1620 1710 35
Snohomish WA 4 5 1.0 6328 800 59
Clark WA 8 6 1.1 2127 460 32
Chelan WA 10 7 1.1 1392 1840 32
Yakima WA 2 10 0.9 11037 4430 52
Franklin WA 7 12 0.9 3709 4090 30
Benton WA 6 15 0.9 3979 2050 33
OR
county ST case rank severity R_e cases cases/100k daily cases
Multnomah OR 1 1 1.0 5045 630 70
Marion OR 3 2 1.1 2989 890 39
Washington OR 2 3 1.0 3178 550 43
Malheur OR 6 4 1.2 818 2690 18
Yamhill OR 10 5 1.2 486 470 16
Umatilla OR 4 6 1.0 2377 3090 39
Jackson OR 9 7 1.1 500 230 15
Clackamas OR 5 8 1.0 1580 390 20
Lane OR 8 10 1.1 604 160 10
Deschutes OR 7 13 1.0 628 350 11
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Los Angeles CA 1 1 1.0 213541 2110 2441
Riverside CA 2 2 1.1 42700 1790 534
Santa Clara CA 10 3 1.2 12729 660 275
Merced CA 20 4 1.2 5761 2140 184
Orange CA 3 5 1.0 40964 1290 432
Sacramento CA 11 6 1.2 11845 780 220
Alameda CA 8 7 1.2 13442 820 221
San Diego CA 5 8 1.0 33513 1010 408
Fresno CA 7 10 1.0 18307 1870 350
San Bernardino CA 4 12 1.0 37188 1740 478
Kern CA 6 13 0.9 25196 2850 538
San Joaquin CA 9 17 1.0 13280 1810 184

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 0.8 128969 3030 1021
Pima AZ 2 2 1.0 18361 1800 215
Cochise AZ 11 3 1.1 1713 1360 22
Yuma AZ 3 4 0.8 11779 5670 75
Yavapai AZ 10 5 1.0 2077 920 29
Mohave AZ 6 6 0.9 3274 1590 27
Pinal AZ 4 7 0.7 8581 2040 48
Apache AZ 7 8 0.9 3220 4500 18
Coconino AZ 8 9 0.9 3128 2230 16
Navajo AZ 5 11 0.8 5419 4990 16
Santa Cruz AZ 9 13 0.8 2689 5770 8
CO
county ST case rank severity R_e cases cases/100k daily cases
El Paso CO 4 1 1.0 5300 770 70
Adams CO 3 2 1.0 6630 1330 66
Denver CO 1 3 0.9 10398 1500 71
Arapahoe CO 2 4 0.9 7442 1170 52
Larimer CO 9 5 1.1 1596 470 23
Jefferson CO 5 6 1.0 4279 750 39
Broomfield CO 15 7 1.2 481 730 8
Weld CO 6 8 1.0 3757 1270 22
Boulder CO 7 9 1.0 2120 660 21
Douglas CO 8 12 0.9 1775 540 14
UT
county ST case rank severity R_e cases cases/100k daily cases
Salt Lake UT 1 1 1.0 21072 1880 179
Utah UT 2 2 1.0 8962 1520 115
Weber UT 4 3 1.0 2855 1150 30
Washington UT 5 4 1.0 2561 1600 25
Davis UT 3 5 0.9 3299 970 34
Cache UT 6 6 1.1 1950 1590 14
Box Elder UT 12 7 1.1 381 720 6
Tooele UT 9 11 0.9 596 910 6
San Juan UT 8 14 0.8 660 4320 4
Summit UT 7 15 0.8 717 1770 2
NM
county ST case rank severity R_e cases cases/100k daily cases
Chaves NM 11 1 1.2 485 740 16
Doña Ana NM 4 2 1.0 2530 1170 34
Lea NM 7 3 1.1 831 1190 22
Bernalillo NM 1 4 0.9 5261 780 45
Eddy NM 14 5 1.2 313 540 7
Curry NM 10 6 1.0 579 1150 13
Santa Fe NM 9 7 1.0 672 450 10
Cibola NM 8 9 0.8 746 2770 18
San Juan NM 3 10 0.9 3064 2400 7
Sandoval NM 5 11 0.8 1154 820 7
McKinley NM 2 12 0.8 4074 5590 8
Otero NM 6 18 0.6 1110 1690 2

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Union NJ 6 1 1.3 16883 3050 15
Bergen NJ 1 2 1.0 21114 2270 38
Hudson NJ 3 3 1.1 19870 2970 22
Essex NJ 2 4 1.0 20027 2520 29
Gloucester NJ 16 5 1.0 3297 1130 22
Camden NJ 9 6 1.0 8686 1710 33
Passaic NJ 5 7 1.0 17846 3540 25
Monmouth NJ 8 9 1.0 10467 1680 29
Middlesex NJ 4 10 1.0 18173 2200 27
Ocean NJ 7 13 0.9 10729 1810 24
PA
county ST case rank severity R_e cases cases/100k daily cases
Union PA 39 1 1.4 255 570 12
Philadelphia PA 1 2 1.0 31568 2000 120
York PA 13 3 1.2 2614 590 34
Northumberland PA 28 4 1.4 483 520 10
Allegheny PA 4 5 0.9 9116 740 98
Delaware PA 3 6 1.0 9414 1670 66
Fayette PA 25 7 1.2 536 410 17
Lancaster PA 6 8 1.0 6007 1120 46
Montgomery PA 2 10 1.0 10207 1240 42
Berks PA 7 14 1.0 5434 1300 27
Bucks PA 5 19 0.9 7274 1160 33
Chester PA 8 20 0.9 5215 1010 32
Lehigh PA 9 23 1.0 4999 1380 18
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore city MD 4 1 1.0 12933 2100 165
Baltimore MD 3 2 1.0 13618 1650 166
Prince George’s MD 1 3 1.0 24492 2700 153
Montgomery MD 2 4 1.0 18615 1790 98
Anne Arundel MD 5 5 1.0 7521 1320 68
Howard MD 6 6 1.0 3933 1250 37
Charles MD 8 7 1.0 2072 1310 22
Harford MD 9 8 1.0 2036 810 27
Frederick MD 7 17 0.9 3107 1250 11
VA
county ST case rank severity R_e cases cases/100k daily cases
Floyd VA 80 1 2.1 75 480 6
Mecklenburg VA 28 2 1.6 442 1430 17
Russell VA 66 3 1.6 133 490 10
Scott VA 71 4 1.6 106 480 8
Wise VA 63 5 1.6 151 390 9
Pittsylvania VA 26 6 1.4 501 810 20
Fairfax VA 1 7 1.1 16522 1440 81
Virginia Beach city VA 4 8 1.0 5236 1160 108
Prince William VA 2 9 1.1 9590 2100 74
Chesterfield VA 5 11 1.1 4455 1310 49
Norfolk city VA 7 12 1.0 3867 1570 68
Loudoun VA 3 13 1.1 5333 1380 33
Henrico VA 6 17 1.0 3963 1220 41
Arlington VA 8 23 1.1 3110 1340 20
Newport News city VA 9 29 0.9 1894 1050 27
WV
county ST case rank severity R_e cases cases/100k daily cases
Logan WV 8 1 1.4 259 770 16
Raleigh WV 7 2 1.3 263 340 10
Grant WV 20 3 1.3 133 1140 7
Cabell WV 4 4 1.1 416 440 10
Kanawha WV 1 5 1.0 973 520 18
Mercer WV 11 6 1.1 214 350 9
Harrison WV 10 7 1.2 232 340 5
Berkeley WV 3 12 1.0 706 620 6
Wood WV 9 15 1.1 252 290 2
Monongalia WV 2 20 0.7 957 910 4
Ohio WV 6 23 0.8 276 650 2
Jefferson WV 5 24 0.8 297 530 1
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 1 7319 1320 49
Sussex DE 2 2 1 5884 2680 24
Kent DE 3 3 1 2313 1320 12

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Clarke AL 38 1 1.8 760 3120 38
Mobile AL 2 2 1.1 10721 2590 245
Jefferson AL 1 3 1.0 13839 2100 228
Washington AL 51 4 1.5 424 2550 15
Montgomery AL 3 5 1.1 7029 3100 87
Jackson AL 28 6 1.2 1078 2070 32
Talladega AL 22 7 1.1 1354 1680 38
Tuscaloosa AL 5 10 1.0 4446 2160 53
Baldwin AL 6 11 0.9 3798 1830 66
Madison AL 4 12 0.9 5686 1590 74
Shelby AL 7 13 1.0 3612 1710 51
Marshall AL 8 14 1.0 3323 3490 40
Lee AL 9 16 1.0 2924 1840 34
MS
county ST case rank severity R_e cases cases/100k daily cases
Harrison MS 3 1 1.1 2635 1300 60
Lee MS 10 2 1.1 1534 1810 39
DeSoto MS 2 3 1.0 3803 2160 65
Stone MS 77 4 1.4 209 1140 8
Tishomingo MS 52 5 1.2 443 2270 17
Marshall MS 36 6 1.1 731 2040 20
Tunica MS 61 7 1.2 373 3670 15
Jackson MS 5 9 0.9 2416 1700 51
Hinds MS 1 11 0.9 5834 2410 70
Washington MS 9 13 1.0 1730 3670 29
Forrest MS 8 14 1.0 1864 2470 29
Jones MS 7 19 0.9 1956 2860 23
Madison MS 4 38 0.8 2495 2410 23
Rankin MS 6 39 0.8 2367 1570 26
LA
county ST case rank severity R_e cases cases/100k daily cases
Lafayette LA 4 1 1.1 7914 3300 152
East Baton Rouge LA 2 2 1.0 12556 2830 171
St. Landry LA 15 3 1.1 2857 3420 70
Jefferson LA 1 4 0.9 15570 3580 126
St. Tammany LA 7 5 1.0 5353 2120 70
Ouachita LA 8 6 1.0 4981 3190 57
Tensas LA 64 7 1.5 88 1890 4
Tangipahoa LA 9 9 1.0 3572 2740 49
Caddo LA 6 11 0.9 6842 2750 66
Orleans LA 3 15 0.9 10852 2790 54
Calcasieu LA 5 26 0.8 7062 3530 73

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Baker FL 51 1 2.0 922 3320 84
Franklin FL 61 2 1.9 500 4260 55
Taylor FL 46 3 1.7 1143 5170 105
Dixie FL 57 4 1.9 554 3370 42
Miami-Dade FL 1 5 0.9 137392 5060 1759
Union FL 66 6 1.7 368 2410 20
Gulf FL 55 7 1.6 712 4430 48
Broward FL 2 9 0.9 64617 3380 819
Palm Beach FL 3 12 0.9 38072 2630 443
Hillsborough FL 4 13 1.0 33378 2420 373
Polk FL 9 16 1.0 14747 2210 206
Duval FL 6 17 1.0 23936 2590 254
Orange FL 5 19 0.9 32370 2450 294
Pinellas FL 7 22 0.9 18264 1910 173
Lee FL 8 31 0.9 16842 2340 127
GA
county ST case rank severity R_e cases cases/100k daily cases
Bleckley GA 118 1 1.8 236 1850 15
Cobb GA 4 2 1.1 14114 1890 289
Gwinnett GA 2 3 1.1 20509 2270 338
Fulton GA 1 4 1.0 21002 2060 336
DeKalb GA 3 5 1.1 14334 1930 217
Cherokee GA 12 6 1.2 3604 1490 90
Richmond GA 9 7 1.1 4598 2280 117
Chatham GA 6 8 1.0 5965 2080 110
Hall GA 5 12 1.0 6261 3200 87
Clayton GA 7 17 1.0 5175 1860 75
Muscogee GA 8 21 1.0 4858 2470 64

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Bee TX 49 1 1.8 1239 3790 103
Brooks TX 162 2 2.1 124 1730 7
Karnes TX 74 3 1.7 711 4620 58
Fort Bend TX 10 4 1.4 10134 1370 326
Harris TX 1 5 1.0 89082 1940 1517
Cameron TX 7 6 1.2 18675 4430 786
Nueces TX 9 7 1.2 15597 4330 381
Tarrant TX 4 8 1.1 35009 1730 656
Dallas TX 2 13 1.0 55835 2160 533
El Paso TX 8 15 1.1 16818 2010 244
Hidalgo TX 6 16 1.0 20546 2420 323
Travis TX 5 20 1.0 23307 1940 228
Bexar TX 3 35 0.7 43960 2280 290
OK
county ST case rank severity R_e cases cases/100k daily cases
Pittsburg OK 26 1 1.6 386 870 28
Tulsa OK 2 2 1.0 10952 1700 219
Oklahoma OK 1 3 1.0 11054 1410 204
Le Flore OK 27 4 1.3 383 770 20
Rogers OK 6 5 1.1 1044 1150 29
Cleveland OK 3 6 0.9 3164 1140 59
Cherokee OK 18 7 1.1 474 980 18
Wagoner OK 7 9 1.1 907 1170 22
Canadian OK 4 12 1.0 1275 930 25
Comanche OK 9 28 0.9 846 690 9
Texas OK 5 38 1.0 1059 5010 3
McCurtain OK 8 43 0.8 868 2630 4

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Muskegon MI 13 1 1.5 1243 720 16
Macomb MI 3 2 1.1 10907 1260 123
Oakland MI 2 3 1.0 15739 1260 112
Wayne MI 1 4 1.0 28526 1620 140
Menominee MI 42 5 1.4 148 640 7
Bay MI 21 6 1.3 642 610 11
Kent MI 4 7 1.0 7646 1190 52
Saginaw MI 8 8 1.1 2054 1070 22
Washtenaw MI 6 10 1.0 3100 850 21
Genesee MI 5 13 0.9 3707 910 23
Ottawa MI 9 14 1.0 1872 660 16
Jackson MI 7 43 0.6 2454 1540 5
WI
county ST case rank severity R_e cases cases/100k daily cases
Milwaukee WI 1 1 1.0 21531 2260 204
Oneida WI 41 2 1.5 143 400 8
Waukesha WI 3 3 1.0 4448 1120 97
Oconto WI 31 4 1.4 261 690 11
Washington WI 11 5 1.1 1130 840 30
Dodge WI 13 6 1.2 848 970 19
Dane WI 2 7 1.0 4620 870 50
Brown WI 4 11 1.0 4334 1670 38
Racine WI 5 14 1.0 3605 1840 45
Outagamie WI 9 19 1.1 1304 710 24
Kenosha WI 6 26 0.9 2736 1630 28
Walworth WI 8 30 0.9 1380 1340 18
Rock WI 7 37 0.9 1589 980 10

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Hennepin MN 1 1 1.0 19718 1600 226
McLeod MN 34 2 1.6 196 550 9
Ramsey MN 2 3 1.1 7745 1430 104
Dakota MN 3 4 1.1 4551 1090 74
St. Louis MN 18 5 1.3 584 290 22
Anoka MN 4 6 1.1 3778 1090 54
Washington MN 6 7 1.1 2185 860 36
Scott MN 9 8 1.1 1614 1130 30
Olmsted MN 8 9 1.1 1765 1150 17
Stearns MN 5 16 1.0 2919 1860 12
Nobles MN 7 36 1.0 1768 8100 3
SD
county ST case rank severity R_e cases cases/100k daily cases
Minnehaha SD 1 1 1.0 4468 2390 30
Charles Mix SD 12 2 1.7 103 1100 0
Meade SD 15 3 1.3 97 350 3
Lincoln SD 3 4 1.0 660 1200 12
Brown SD 5 5 1.2 450 1160 5
Codington SD 8 6 1.3 136 490 2
Brookings SD 7 7 1.3 139 410 2
Pennington SD 2 8 1.0 904 830 8
Union SD 6 12 1.0 219 1440 3
Clay SD 9 13 1.1 131 940 2
Beadle SD 4 14 1.0 594 3230 1
ND
county ST case rank severity R_e cases cases/100k daily cases
Sioux ND 14 1 2.1 86 1950 5
Rolette ND 15 2 2.0 81 550 8
Morton ND 4 3 1.3 392 1280 15
Burleigh ND 2 4 1.1 1251 1330 36
Stark ND 5 5 1.3 287 930 12
Ramsey ND 16 6 1.3 80 690 4
Ward ND 7 7 1.1 240 350 7
Cass ND 1 8 1.0 3069 1760 16
Grand Forks ND 3 9 1.0 694 990 7
Williams ND 6 10 1.0 280 820 5
Mountrail ND 9 11 1.1 136 1340 2
Benson ND 8 15 0.6 145 2110 3

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
New Haven CT 2 1 1.0 13224 1540 19
Fairfield CT 1 2 0.8 18095 1920 33
New London CT 5 3 1.1 1457 540 6
Hartford CT 3 4 0.8 12858 1440 23
Windham CT 8 5 1.0 744 640 5
Middlesex CT 6 6 1.1 1406 860 2
Tolland CT 7 7 0.8 1072 710 5
Litchfield CT 4 8 0.8 1621 890 3
MA
county ST case rank severity R_e cases cases/100k daily cases
Suffolk MA 2 1 1.1 21993 2780 75
Essex MA 3 2 1.1 17945 2300 66
Middlesex MA 1 3 1.0 26566 1670 80
Norfolk MA 5 4 1.0 10718 1530 44
Bristol MA 6 5 1.0 9423 1690 33
Worcester MA 4 6 1.0 13708 1670 36
Plymouth MA 7 7 1.0 9285 1810 19
Hampden MA 8 8 1.0 7647 1630 22
Barnstable MA 9 10 0.9 1816 850 6
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 0.9 15183 2390 60
Kent RI 2 2 0.9 1513 920 9
Newport RI 4 3 1.0 400 480 2
Washington RI 3 4 0.9 611 480 2
Bristol RI 5 5 0.9 319 650 1

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
New York City NY 1 1 1.0 233080 2760 293
Suffolk NY 2 2 1.0 43919 2950 63
Erie NY 7 3 1.1 8974 980 45
Nassau NY 3 4 1.0 43756 3230 48
Monroe NY 8 5 1.0 5005 670 27
Westchester NY 4 6 1.0 36267 3740 31
Niagara NY 14 7 1.1 1508 710 6
Dutchess NY 9 9 1.0 4632 1580 12
Rockland NY 5 10 1.1 13954 4310 8
Orange NY 6 13 1.0 11196 2960 10

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Rutland VT 4 1 1.1 101 170 1
Chittenden VT 1 2 0.9 732 450 1
Windham VT 3 3 1.3 103 240 0
Bennington VT 5 4 1.1 88 240 0
Franklin VT 2 5 0.6 119 240 0
ME
county ST case rank severity R_e cases cases/100k daily cases
Cumberland ME 1 1 0.9 2096 720 5
Androscoggin ME 3 2 1.0 565 530 2
York ME 2 3 0.9 681 340 3
Kennebec ME 4 4 0.8 173 140 1
Penobscot ME 5 5 0.7 154 100 1
NH
county ST case rank severity R_e cases cases/100k daily cases
Strafford NH 4 1 1.2 361 280 4
Hillsborough NH 1 2 0.9 3876 940 12
Rockingham NH 2 3 1.0 1703 560 8
Cheshire NH 7 4 1.1 101 130 1
Merrimack NH 3 5 1.0 466 310 1
Belknap NH 5 6 0.9 117 190 1
Carroll NH 8 7 0.8 96 200 1
Grafton NH 6 8 0.6 104 120 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Richland SC 3 1 1.0 9042 2210 122
Greenville SC 2 2 1.0 11140 2240 102
Beaufort SC 6 3 1.0 4288 2350 78
Florence SC 10 4 1.0 3594 2590 70
Charleston SC 1 5 0.9 12556 3180 104
Aiken SC 15 6 1.1 1973 1180 47
York SC 9 7 1.0 3678 1420 55
Spartanburg SC 8 8 1.0 4181 1380 47
Horry SC 4 10 0.9 8751 2730 71
Berkeley SC 7 14 0.9 4283 2050 46
Lexington SC 5 17 0.9 5086 1780 48
NC
county ST case rank severity R_e cases cases/100k daily cases
Mecklenburg NC 1 1 0.9 22683 2150 199
Wake NC 2 2 1.0 12366 1180 137
Northampton NC 67 3 1.4 346 1710 9
Wilkes NC 43 4 1.3 834 1220 12
Haywood NC 59 5 1.2 461 760 19
Cumberland NC 8 6 1.0 3205 970 56
Guilford NC 4 7 1.0 5764 1100 63
Union NC 9 10 1.0 3191 1410 44
Forsyth NC 5 11 1.0 5350 1440 49
Gaston NC 6 13 1.0 3415 1580 44
Durham NC 3 16 1.0 6265 2040 44
Johnston NC 7 17 1.0 3398 1780 42

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Yellowstone MT 1 1 1.1 1341 850 32
Silver Bow MT 9 2 1.4 102 290 6
Flathead MT 4 3 1.2 365 370 14
Missoula MT 5 4 1.1 348 300 10
Big Horn MT 3 5 1.0 465 3480 16
Glacier MT 13 6 1.3 78 570 3
Gallatin MT 2 7 0.9 977 930 11
Lewis and Clark MT 8 8 1.0 168 250 4
Cascade MT 7 10 0.8 177 220 3
Lake MT 6 13 0.8 187 630 2
WY
county ST case rank severity R_e cases cases/100k daily cases
Washakie WY 13 1 1.6 77 950 4
Sheridan WY 12 2 1.1 78 260 2
Carbon WY 9 3 1.0 106 680 3
Laramie WY 1 4 0.9 512 520 5
Fremont WY 2 5 0.9 510 1270 3
Natrona WY 6 6 0.9 239 300 2
Uinta WY 4 7 0.9 283 1370 3
Campbell WY 8 8 1.0 127 270 1
Park WY 7 9 0.9 137 470 2
Teton WY 3 10 0.7 390 1690 4
Sweetwater WY 5 11 0.8 267 610 2
ID
county ST case rank severity R_e cases cases/100k daily cases
Bonneville ID 5 1 1.3 1182 1050 59
Canyon ID 2 2 1.1 6051 2850 131
Ada ID 1 3 1.0 9316 2090 142
Shoshone ID 22 4 1.6 112 890 6
Twin Falls ID 4 5 1.1 1452 1740 27
Jefferson ID 15 6 1.2 220 790 10
Kootenai ID 3 7 1.0 1882 1230 34
Jerome ID 9 14 1.1 492 2100 8
Cassia ID 7 17 1.0 541 2290 7
Minidoka ID 8 20 0.9 495 2400 6
Blaine ID 6 25 1.0 578 2630 1

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Franklin OH 1 1 1.0 18763 1470 185
Cuyahoga OH 2 2 1.0 13802 1100 130
Hamilton OH 3 3 1.0 9801 1210 79
Lucas OH 4 4 1.0 5561 1290 86
Champaign OH 58 5 1.4 193 500 10
Summit OH 6 6 1.0 3653 670 46
Lawrence OH 45 7 1.3 298 490 10
Montgomery OH 5 8 1.0 4489 840 57
Butler OH 7 11 1.0 3003 790 37
Mahoning OH 9 17 1.0 2594 1120 22
Marion OH 8 51 1.0 2941 4500 7
IL
county ST case rank severity R_e cases cases/100k daily cases
Cook IL 1 1 1.1 112591 2160 680
Tazewell IL 22 2 1.5 591 440 31
LaSalle IL 17 3 1.4 814 740 38
Jefferson IL 35 4 1.5 296 780 14
Jersey IL 58 5 1.7 114 520 6
DuPage IL 3 6 1.1 12435 1330 108
Will IL 5 7 1.1 9412 1370 92
Madison IL 9 10 1.1 2714 1020 64
Lake IL 2 12 1.0 12863 1830 95
Kane IL 4 14 1.1 9957 1880 79
St. Clair IL 6 17 1.1 4552 1730 72
McHenry IL 8 22 1.0 3296 1070 37
Winnebago IL 7 45 0.9 3821 1340 16
IN
county ST case rank severity R_e cases cases/100k daily cases
Sullivan IN 71 1 1.8 134 640 8
Marion IN 1 2 1.1 16300 1730 167
Vigo IN 27 3 1.4 696 650 30
Lake IN 2 4 1.1 7736 1590 72
St. Joseph IN 5 5 1.1 3601 1340 57
Allen IN 4 6 1.1 4000 1080 46
Carroll IN 57 7 1.5 197 990 8
Hamilton IN 6 10 1.1 2874 910 43
Vanderburgh IN 7 12 1.0 2052 1130 40
Elkhart IN 3 14 1.0 5010 2460 38
Hendricks IN 8 15 1.1 1945 1210 21
Johnson IN 9 20 1.1 1819 1200 17

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Weakley TN 48 1 1.5 512 1520 37
Johnson TN 59 2 1.6 333 1870 28
Shelby TN 1 3 1.0 24326 2600 328
Davidson TN 2 4 1.0 23428 3430 210
Knox TN 5 5 1.0 5158 1130 125
Benton TN 75 6 1.5 178 1100 13
Madison TN 20 7 1.2 1205 1230 46
Hamilton TN 4 12 1.0 6328 1770 82
Rutherford TN 3 21 0.9 6723 2190 77
Williamson TN 6 27 1.0 3636 1660 45
Wilson TN 8 29 1.0 2356 1780 33
Montgomery TN 9 31 1.0 2017 1030 37
Sumner TN 7 33 1.0 3509 1960 37
KY
county ST case rank severity R_e cases cases/100k daily cases
Jefferson KY 1 1 1.1 8582 1120 174
Fayette KY 2 2 1.1 4074 1280 93
Madison KY 14 3 1.3 535 600 19
Hardin KY 9 4 1.3 667 620 18
Calloway KY 30 5 1.3 253 650 9
Washington KY 70 6 1.4 89 740 4
Warren KY 3 7 1.0 2697 2130 30
Kenton KY 4 10 1.0 1463 890 18
Shelby KY 7 13 1.1 782 1670 9
Boone KY 5 16 1.0 1125 870 12
Christian KY 8 17 1.0 668 920 11
Daviess KY 6 30 1.0 784 780 8

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
St. Louis MO 1 1 1.0 15569 1560 272
Jackson MO 4 2 1.0 4263 620 101
Taney MO 15 3 1.2 664 1210 33
Greene MO 6 4 1.2 1645 570 45
St. Louis city MO 2 5 1.0 5390 1730 90
Jefferson MO 5 6 1.1 1912 860 52
Cole MO 19 7 1.3 441 570 17
St. Charles MO 3 8 0.9 4330 1110 75
Boone MO 7 11 1.1 1467 830 28
Jasper MO 8 49 0.8 1283 1080 8
Buchanan MO 9 50 0.9 1094 1230 4
AR
county ST case rank severity R_e cases cases/100k daily cases
Jackson AR 54 1 2.3 120 700 11
Poinsett AR 32 2 1.6 296 1230 19
Logan AR 33 3 1.5 290 1330 17
Pulaski AR 2 4 1.1 5746 1460 99
Sebastian AR 4 5 1.1 2314 1820 64
Mississippi AR 14 6 1.2 1093 2550 41
Independence AR 22 7 1.2 592 1590 30
Craighead AR 7 10 1.1 1415 1340 34
Jefferson AR 5 13 1.1 1596 2270 30
Hot Spring AR 6 14 1.3 1549 4620 10
Crittenden AR 8 21 1.0 1403 2860 21
Benton AR 3 22 0.9 4852 1870 36
Washington AR 1 23 0.9 6390 2800 40
Pope AR 9 35 0.9 1356 2130 17

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 3100.8 seconds to compute.
2020-08-12 08:35:56

version history

Today is 2020-08-12.
84 days ago: Multiple states.
76 days ago: \(R_e\) computation.
73 days ago: created color coding for \(R_e\) plots.
68 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
68 days ago: “persistence” time evolution.
61 days ago: “In control” mapping.
61 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
53 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
48 days ago: Added Per Capita US Map.
46 days ago: Deprecated national map.
42 days ago: added state “Hot 10” analysis.
37 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
35 days ago: added per capita disease and mortaility to state-level analysis.
23 days ago: changed to county boundaries on national map for per capita disease.
18 days ago: corrected factor of two error in death trend data.
14 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
9 days ago: added county level “baseline control” and \(R-e\) maps.
5 days ago: fixed normalization error on total disease stats plot.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.